{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Censored Data Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.6\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pymc3 as pm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import theano.tensor as tt\n",
    "\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "print('Running on PyMC3 v{}'.format(pm.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[This example notebook on Bayesian survival\n",
    "analysis](http://docs.pymc.io/notebooks/survival_analysis.html) touches on the\n",
    "point of censored data. _Censoring_ is a form of missing-data problem, in which\n",
    "observations greater than a certain threshold are clipped down to that\n",
    "threshold, or observations less than a certain threshold are clipped up to that\n",
    "threshold, or both. These are called right, left and interval censoring,\n",
    "respectively. In this example notebook we consider interval censoring.\n",
    "\n",
    "Censored data arises in many modelling problems. Two common examples are:\n",
    "\n",
    "1. _Survival analysis:_ when studying the effect of a certain medical treatment\n",
    "   on survival times, it is impossible to prolong the study until all subjects\n",
    "   have died. At the end of the study, the only data collected for many patients\n",
    "   is that they were still alive for a time period $T$ after the treatment was\n",
    "   administered: in reality, their true survival times are greater than $T$.\n",
    "\n",
    "2. _Sensor saturation:_ a sensor might have a limited range and the upper and\n",
    "   lower limits would simply be the highest and lowest values a sensor can\n",
    "   report. For instance, many mercury thermometers only report a very narrow\n",
    "   range of temperatures.\n",
    "\n",
    "This example notebook presents two different ways of dealing with censored data\n",
    "in PyMC3:\n",
    "\n",
    "1. An imputed censored model, which represents censored data as parameters and\n",
    "   makes up plausible values for all censored values. As a result of this\n",
    "   imputation, this model is capable of generating plausible sets of made-up\n",
    "   values that would have been censored. Each censored element introduces a\n",
    "   random variable.\n",
    "\n",
    "2. An unimputed censored model, where the censored data are integrated out and\n",
    "   accounted for only through the log-likelihood. This method deals more\n",
    "   adequately with large amounts of censored data and converges more quickly.\n",
    "\n",
    "To establish a baseline we compare to an uncensored model of the uncensored\n",
    "data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Produce normally distributed samples\n",
    "np.random.seed(1618)\n",
    "size = 500\n",
    "mu = 13.\n",
    "sigma = 5.\n",
    "samples = np.random.normal(mu, sigma, size)\n",
    "\n",
    "# Set censoring limits\n",
    "high = 16.\n",
    "low = -1.\n",
    "\n",
    "# Censor samples\n",
    "censored = samples[(samples > low) & (samples < high)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize uncensored and censored data\n",
    "_, axarr = plt.subplots(ncols=2, figsize=[16, 4], sharex=True, sharey=True)\n",
    "for i, data in enumerate([samples, censored]):\n",
    "    sns.distplot(data, ax=axarr[i])\n",
    "axarr[0].set_title('Uncensored')\n",
    "axarr[1].set_title('Censored')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline - Uncensored Model of Uncensored Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncensored model\n",
    "with pm.Model() as uncensored_model:\n",
    "    mu = pm.Normal('mu', mu=0., sigma=(high - low) / 2.)\n",
    "    sigma = pm.HalfNormal('sigma', sigma=(high - low) / 2.)\n",
    "    observed = pm.Normal('observed', mu=mu, sigma=sigma, observed=samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1 - Imputed Censored Model of Censored Data\n",
    "\n",
    "In this model, we impute the censored values from the same distribution as the uncensored data. Sampling from the posterior generates possible uncensored data sets.\n",
    "\n",
    "This model makes use of [PyMC3's bounded variables](https://docs.pymc.io/api/bounds.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputed censored model\n",
    "n_right_censored = len(samples[samples >= high])\n",
    "n_left_censored = len(samples[samples <= low])\n",
    "n_observed = len(samples) - n_right_censored - n_left_censored\n",
    "\n",
    "with pm.Model() as imputed_censored_model:\n",
    "    mu = pm.Normal('mu', mu=0., sigma=(high - low) / 2.)\n",
    "    sigma = pm.HalfNormal('sigma', sigma=(high - low) / 2.)\n",
    "    \n",
    "    right_censored = pm.Bound(pm.Normal, lower=high)(\n",
    "        'right_censored', mu=mu, sigma=sigma, shape=n_right_censored\n",
    "    )\n",
    "    left_censored = pm.Bound(pm.Normal, upper=low)(\n",
    "        'left_censored', mu=mu, sigma=sigma, shape=n_left_censored\n",
    "    )\n",
    "    \n",
    "    observed = pm.Normal(\n",
    "        'observed',\n",
    "        mu=mu,\n",
    "        sigma=sigma,\n",
    "        observed=censored,\n",
    "        shape=n_observed\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2 - Unimputed Censored Model of Censored Data\n",
    "\n",
    "In this model, we do not impute censored data, but instead integrate them out through the likelihood.\n",
    "\n",
    "The implementations of the likelihoods are non-trivial. See the [Stan manual](https://github.com/stan-dev/stan/releases/download/v2.14.0/stan-reference-2.14.0.pdf) (section 11.3 on censored data) and the [original PyMC3 issue on GitHub](https://github.com/pymc-devs/pymc3/issues/1833) for more information.\n",
    "\n",
    "This model makes use of [PyMC3's `Potential`](https://docs.pymc.io/api/model.html#pymc3.model.Potential)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the log cdf and log complementary cdf of the normal Distribution from PyMC3\n",
    "from pymc3.distributions.dist_math import normal_lcdf, normal_lccdf \n",
    "\n",
    "# Helper functions for unimputed censored model\n",
    "def left_censored_likelihood(mu, sigma, n_left_censored, lower_bound):\n",
    "    ''' Likelihood of left-censored data. '''\n",
    "    return n_left_censored * normal_lcdf(mu, sigma, lower_bound)\n",
    "\n",
    "\n",
    "def right_censored_likelihood(mu, sigma, n_right_censored, upper_bound):\n",
    "    ''' Likelihood of right-censored data. '''\n",
    "    return n_right_censored * normal_lccdf(mu, sigma, upper_bound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unimputed censored model\n",
    "with pm.Model() as unimputed_censored_model:\n",
    "    mu = pm.Normal('mu', mu=0., sigma=(high - low) / 2.)\n",
    "    sigma = pm.HalfNormal('sigma', sigma=(high - low) / 2.)\n",
    "    \n",
    "    observed = pm.Normal(\n",
    "        'observed',\n",
    "        mu=mu,\n",
    "        sigma=sigma,\n",
    "        observed=censored,\n",
    "    )\n",
    "    \n",
    "    left_censored = pm.Potential(\n",
    "        'left_censored',\n",
    "        left_censored_likelihood(mu, sigma, n_left_censored, low)\n",
    "    )\n",
    "    right_censored = pm.Potential(\n",
    "        'right_censored',\n",
    "        right_censored_likelihood(mu, sigma, n_right_censored, high)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, mu]\n",
      "Sampling 4 chains: 100%|██████████| 6000/6000 [00:01<00:00, 5790.89draws/s]\n",
      "/home/canyon/miniconda3/envs/pymc/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  chain_likelihoods.append(np.stack(log_like))\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 993.6x331.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Uncensored model\n",
    "with uncensored_model:\n",
    "    trace = pm.sample(tune=1000)  # Increase `tune` to avoid divergences\n",
    "    pm.plot_posterior(trace);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [left_censored, right_censored, sigma, mu]\n",
      "Sampling 4 chains: 100%|██████████| 4000/4000 [00:03<00:00, 1127.48draws/s]\n",
      "/home/canyon/repos/pymc3/pymc3/plots/__init__.py:40: UserWarning: Keyword argument `varnames` renamed to `var_names`, and will be removed in pymc3 3.8\n",
      "  warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))\n",
      "/home/canyon/miniconda3/envs/pymc/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  chain_likelihoods.append(np.stack(log_like))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 993.6x331.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Imputed censored model\n",
    "with imputed_censored_model:\n",
    "    trace = pm.sample()\n",
    "    pm.plot_posterior(trace, varnames=['mu', 'sigma']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [sigma, mu]\n",
      "Sampling 4 chains: 100%|██████████| 6000/6000 [00:01<00:00, 5044.47draws/s]\n",
      "/home/canyon/repos/pymc3/pymc3/plots/__init__.py:40: UserWarning: Keyword argument `varnames` renamed to `var_names`, and will be removed in pymc3 3.8\n",
      "  warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))\n",
      "/home/canyon/miniconda3/envs/pymc/lib/python3.7/site-packages/arviz/data/io_pymc3.py:56: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
      "  chain_likelihoods.append(np.stack(log_like))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 993.6x331.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unimputed censored model\n",
    "with unimputed_censored_model:\n",
    "    trace = pm.sample(tune=1000)  # Increase `tune` to avoid divergences\n",
    "    pm.plot_posterior(trace, varnames=['mu', 'sigma']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "As we can see, both censored models appear to capture the mean and variance of the underlying distribution as well as the uncensored model! In addition, the imputed censored model is capable of generating data sets of censored values (sample from the posteriors of `left_censored` and `right_censored` to generate them), while the unimputed censored model scales much better with more censored data, and converges faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "\n",
    "- Originally authored by [Luis Mario Domenzain](https://github.com/domenzain) on Mar 7, 2017.\n",
    "\n",
    "- Updated by [George Ho](https://github.com/eigenfoo) on Jul 14, 2018."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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